Overview

Dataset statistics

Number of variables15
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.7 KiB
Average record size in memory64.3 B

Variable types

Categorical9
Numeric6

Alerts

feat.e is highly overall correlated with feat.iHigh correlation
feat.f is highly overall correlated with responseHigh correlation
feat.i is highly overall correlated with feat.eHigh correlation
response is highly overall correlated with feat.fHigh correlation
feat.g_x is highly overall correlated with feat.g_y and 1 other fieldsHigh correlation
feat.g_y is highly overall correlated with feat.g_x and 1 other fieldsHigh correlation
feat.g_z is highly overall correlated with feat.g_x and 1 other fieldsHigh correlation
feat.c_a is highly overall correlated with feat.c_bHigh correlation
feat.c_b is highly overall correlated with feat.c_a and 1 other fieldsHigh correlation
feat.c_d is highly overall correlated with feat.c_bHigh correlation
feat.a has unique valuesUnique
feat.e has unique valuesUnique
feat.f has unique valuesUnique
feat.h has unique valuesUnique
feat.i has unique valuesUnique

Reproduction

Analysis started2022-11-23 20:34:54.919224
Analysis finished2022-11-23 20:35:06.666927
Duration11.75 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

response
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
1
553 
0
447 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 553
55.3%
0 447
44.7%

Length

2022-11-23T15:35:06.800050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-23T15:35:07.007596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 553
55.3%
0 447
44.7%

Most occurring characters

ValueCountFrequency (%)
1 553
55.3%
0 447
44.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 553
55.3%
0 447
44.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 553
55.3%
0 447
44.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 553
55.3%
0 447
44.7%

feat.a
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0483836
Minimum-7.429324
Maximum10.72312
Zeros0
Zeros (%)0.0%
Negative353
Negative (%)35.3%
Memory size7.9 KiB
2022-11-23T15:35:07.225897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-7.429324
5-th percentile-3.8677529
Q1-0.88497273
median1.0276289
Q32.9938056
95-th percentile6.0284016
Maximum10.72312
Range18.152444
Interquartile range (IQR)3.8787783

Descriptive statistics

Standard deviation2.9750849
Coefficient of variation (CV)2.8377828
Kurtosis-0.068601967
Mean1.0483836
Median Absolute Deviation (MAD)1.950913
Skewness0.065392043
Sum1048.3836
Variance8.8511303
MonotonicityNot monotonic
2022-11-23T15:35:07.484731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.6814269397 1
 
0.1%
0.1177140185 1
 
0.1%
3.307156885 1
 
0.1%
1.362157988 1
 
0.1%
3.590945302 1
 
0.1%
5.141543585 1
 
0.1%
6.898744046 1
 
0.1%
0.9148148358 1
 
0.1%
-5.747153271 1
 
0.1%
1.094578014 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
-7.429324037 1
0.1%
-6.982768395 1
0.1%
-6.929446856 1
0.1%
-6.805099011 1
0.1%
-6.523753407 1
0.1%
-6.397694581 1
0.1%
-5.927506627 1
0.1%
-5.747153271 1
0.1%
-5.674963089 1
0.1%
-5.631899332 1
0.1%
ValueCountFrequency (%)
10.7231198 1
0.1%
9.07514201 1
0.1%
9.054576998 1
0.1%
8.726349291 1
0.1%
8.714374438 1
0.1%
8.65907834 1
0.1%
8.463993632 1
0.1%
8.374181476 1
0.1%
8.290679957 1
0.1%
8.250320061 1
0.1%

feat.b
Real number (ℝ)

Distinct993
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.9413248
Minimum-8.5717913
Maximum1.0855562
Zeros0
Zeros (%)0.0%
Negative995
Negative (%)99.5%
Memory size7.9 KiB
2022-11-23T15:35:07.747975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-8.5717913
5-th percentile-6.4825299
Q1-4.9787453
median-3.9177214
Q3-2.8932498
95-th percentile-1.6018393
Maximum1.0855562
Range9.6573476
Interquartile range (IQR)2.0854955

Descriptive statistics

Standard deviation1.5066016
Coefficient of variation (CV)-0.38225765
Kurtosis-0.060238988
Mean-3.9413248
Median Absolute Deviation (MAD)1.04813
Skewness-0.023163969
Sum-3941.3248
Variance2.2698483
MonotonicityNot monotonic
2022-11-23T15:35:07.994292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.917721434 8
 
0.8%
-5.493698087 1
 
0.1%
-2.481194209 1
 
0.1%
-2.667889449 1
 
0.1%
-6.909910232 1
 
0.1%
-2.465197367 1
 
0.1%
-3.99181341 1
 
0.1%
-3.145331546 1
 
0.1%
-6.479883345 1
 
0.1%
-4.99998157 1
 
0.1%
Other values (983) 983
98.3%
ValueCountFrequency (%)
-8.571791335 1
0.1%
-8.042994054 1
0.1%
-7.943988162 1
0.1%
-7.906057256 1
0.1%
-7.824014162 1
0.1%
-7.693862525 1
0.1%
-7.566110388 1
0.1%
-7.503920998 1
0.1%
-7.470603661 1
0.1%
-7.376567763 1
0.1%
ValueCountFrequency (%)
1.085556232 1
0.1%
0.935776165 1
0.1%
0.7760667111 1
0.1%
0.224126414 1
0.1%
0.1960867204 1
0.1%
-0.1340978557 1
0.1%
-0.281881298 1
0.1%
-0.3280029895 1
0.1%
-0.3632662883 1
0.1%
-0.3756889403 1
0.1%

feat.d
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1.0
520 
0.0
480 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 520
52.0%
0.0 480
48.0%

Length

2022-11-23T15:35:08.223584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-23T15:35:08.425867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 520
52.0%
0.0 480
48.0%

Most occurring characters

ValueCountFrequency (%)
0 1480
49.3%
. 1000
33.3%
1 520
 
17.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
66.7%
Other Punctuation 1000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1480
74.0%
1 520
 
26.0%
Other Punctuation
ValueCountFrequency (%)
. 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1480
49.3%
. 1000
33.3%
1 520
 
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1480
49.3%
. 1000
33.3%
1 520
 
17.3%

feat.e
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.51832075
Minimum-6.7581764
Maximum5.2897088
Zeros0
Zeros (%)0.0%
Negative596
Negative (%)59.6%
Memory size7.9 KiB
2022-11-23T15:35:08.614124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-6.7581764
5-th percentile-3.8409138
Q1-1.7792963
median-0.51638151
Q30.80107757
95-th percentile2.7744859
Maximum5.2897088
Range12.047885
Interquartile range (IQR)2.5803738

Descriptive statistics

Standard deviation1.9847034
Coefficient of variation (CV)-3.8291026
Kurtosis-0.034369636
Mean-0.51832075
Median Absolute Deviation (MAD)1.2858637
Skewness-0.071509945
Sum-518.32075
Variance3.9390475
MonotonicityNot monotonic
2022-11-23T15:35:08.866742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8006149557 1
 
0.1%
-0.4105429274 1
 
0.1%
-2.149190977 1
 
0.1%
0.6691611674 1
 
0.1%
-2.496597332 1
 
0.1%
-3.468563012 1
 
0.1%
0.01555496617 1
 
0.1%
0.3305800081 1
 
0.1%
1.550839146 1
 
0.1%
0.9521521358 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
-6.758176383 1
0.1%
-6.399743569 1
0.1%
-6.335952428 1
0.1%
-6.178752334 1
0.1%
-5.856328822 1
0.1%
-5.819338759 1
0.1%
-5.719050018 1
0.1%
-5.473047809 1
0.1%
-5.271585502 1
0.1%
-5.166574755 1
0.1%
ValueCountFrequency (%)
5.289708782 1
0.1%
4.807481457 1
0.1%
4.698983411 1
0.1%
4.696980464 1
0.1%
4.628818601 1
0.1%
4.580737248 1
0.1%
4.466210225 1
0.1%
4.245676957 1
0.1%
3.971205537 1
0.1%
3.96399422 1
0.1%

feat.f
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.2573455
Minimum-31.099076
Maximum21.567936
Zeros0
Zeros (%)0.0%
Negative789
Negative (%)78.9%
Memory size7.9 KiB
2022-11-23T15:35:09.186153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-31.099076
5-th percentile-19.487967
Q1-11.654848
median-6.2624289
Q3-0.91253298
95-th percentile6.3077715
Maximum21.567936
Range52.667012
Interquartile range (IQR)10.742315

Descriptive statistics

Standard deviation8.0055295
Coefficient of variation (CV)-1.2793811
Kurtosis0.17362413
Mean-6.2573455
Median Absolute Deviation (MAD)5.3710946
Skewness0.027865522
Sum-6257.3455
Variance64.088503
MonotonicityNot monotonic
2022-11-23T15:35:09.489488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.427601788 1
 
0.1%
5.733868313 1
 
0.1%
-16.23534137 1
 
0.1%
-4.527911115 1
 
0.1%
-11.99220613 1
 
0.1%
-10.86518825 1
 
0.1%
-2.641091002 1
 
0.1%
0.7347983651 1
 
0.1%
-2.958744506 1
 
0.1%
-10.27875464 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
-31.09907622 1
0.1%
-30.34507874 1
0.1%
-29.10103863 1
0.1%
-28.74414316 1
0.1%
-28.02887003 1
0.1%
-25.93349585 1
0.1%
-25.90821945 1
0.1%
-25.61192808 1
0.1%
-25.58897083 1
0.1%
-25.03581109 1
0.1%
ValueCountFrequency (%)
21.56793583 1
0.1%
20.17426201 1
0.1%
19.88434253 1
0.1%
17.93220262 1
0.1%
17.77268027 1
0.1%
17.3905916 1
0.1%
14.17918456 1
0.1%
14.01412077 1
0.1%
13.32045114 1
0.1%
13.04330909 1
0.1%

feat.h
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.030833
Minimum3.4212483
Maximum17.431441
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-11-23T15:35:09.735619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.4212483
5-th percentile6.6924294
Q18.7001442
median10.028309
Q311.528759
95-th percentile13.25505
Maximum17.431441
Range14.010193
Interquartile range (IQR)2.8286147

Descriptive statistics

Standard deviation2.0222002
Coefficient of variation (CV)0.20159843
Kurtosis0.039840298
Mean10.030833
Median Absolute Deviation (MAD)1.4211137
Skewness-0.13026894
Sum10030.833
Variance4.0892935
MonotonicityNot monotonic
2022-11-23T15:35:10.008088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.25419887 1
 
0.1%
6.754303427 1
 
0.1%
11.24278619 1
 
0.1%
12.68298848 1
 
0.1%
9.352376556 1
 
0.1%
10.28876694 1
 
0.1%
7.836294432 1
 
0.1%
9.988045316 1
 
0.1%
9.277988632 1
 
0.1%
8.493310924 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
3.421248303 1
0.1%
3.475701331 1
0.1%
3.945908562 1
0.1%
4.151417736 1
0.1%
4.22770175 1
0.1%
4.51677224 1
0.1%
4.614397502 1
0.1%
4.86227061 1
0.1%
5.022192772 1
0.1%
5.217552022 1
0.1%
ValueCountFrequency (%)
17.43144145 1
0.1%
16.16747908 1
0.1%
15.85780148 1
0.1%
15.69748807 1
0.1%
14.83414122 1
0.1%
14.79040265 1
0.1%
14.62396292 1
0.1%
14.62363889 1
0.1%
14.48832726 1
0.1%
14.47632645 1
0.1%

feat.i
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.5186067
Minimum-6.7634268
Maximum5.3157286
Zeros0
Zeros (%)0.0%
Negative598
Negative (%)59.8%
Memory size7.9 KiB
2022-11-23T15:35:10.331547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-6.7634268
5-th percentile-3.8868159
Q1-1.773089
median-0.50608495
Q30.80304068
95-th percentile2.7870572
Maximum5.3157286
Range12.079155
Interquartile range (IQR)2.5761297

Descriptive statistics

Standard deviation1.9843781
Coefficient of variation (CV)-3.8263643
Kurtosis-0.029327021
Mean-0.5186067
Median Absolute Deviation (MAD)1.2926208
Skewness-0.071304314
Sum-518.6067
Variance3.9377566
MonotonicityNot monotonic
2022-11-23T15:35:10.814513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8280728697 1
 
0.1%
-0.3358788662 1
 
0.1%
-2.138754969 1
 
0.1%
0.7567881697 1
 
0.1%
-2.505539361 1
 
0.1%
-3.490892139 1
 
0.1%
0.07054932741 1
 
0.1%
0.3639592021 1
 
0.1%
1.607569457 1
 
0.1%
0.9758482567 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
-6.763426764 1
0.1%
-6.398746196 1
0.1%
-6.376277409 1
0.1%
-6.192309923 1
0.1%
-5.845633414 1
0.1%
-5.82995219 1
0.1%
-5.756198292 1
0.1%
-5.522454828 1
0.1%
-5.215154372 1
0.1%
-5.174046974 1
0.1%
ValueCountFrequency (%)
5.315728559 1
0.1%
4.842965736 1
0.1%
4.715903484 1
0.1%
4.646257329 1
0.1%
4.588374531 1
0.1%
4.550044682 1
0.1%
4.446508874 1
0.1%
4.248004011 1
0.1%
3.966186668 1
0.1%
3.947004253 1
0.1%

feat.c_a
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
760 
1
240 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 760
76.0%
1 240
 
24.0%

Length

2022-11-23T15:35:11.026720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-23T15:35:11.248678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 760
76.0%
1 240
 
24.0%

Most occurring characters

ValueCountFrequency (%)
0 760
76.0%
1 240
 
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 760
76.0%
1 240
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 760
76.0%
1 240
 
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 760
76.0%
1 240
 
24.0%

feat.c_b
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
722 
1
278 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 722
72.2%
1 278
 
27.8%

Length

2022-11-23T15:35:11.411110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-23T15:35:11.611896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 722
72.2%
1 278
 
27.8%

Most occurring characters

ValueCountFrequency (%)
0 722
72.2%
1 278
 
27.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 722
72.2%
1 278
 
27.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 722
72.2%
1 278
 
27.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 722
72.2%
1 278
 
27.8%

feat.c_c
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
773 
1
227 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 773
77.3%
1 227
 
22.7%

Length

2022-11-23T15:35:11.776932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-23T15:35:11.982135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 773
77.3%
1 227
 
22.7%

Most occurring characters

ValueCountFrequency (%)
0 773
77.3%
1 227
 
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 773
77.3%
1 227
 
22.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 773
77.3%
1 227
 
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 773
77.3%
1 227
 
22.7%

feat.c_d
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
745 
1
255 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 745
74.5%
1 255
 
25.5%

Length

2022-11-23T15:35:12.152157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-23T15:35:12.395872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 745
74.5%
1 255
 
25.5%

Most occurring characters

ValueCountFrequency (%)
0 745
74.5%
1 255
 
25.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 745
74.5%
1 255
 
25.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 745
74.5%
1 255
 
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 745
74.5%
1 255
 
25.5%

feat.g_x
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
670 
1
330 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 670
67.0%
1 330
33.0%

Length

2022-11-23T15:35:12.550390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-23T15:35:12.732893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 670
67.0%
1 330
33.0%

Most occurring characters

ValueCountFrequency (%)
0 670
67.0%
1 330
33.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 670
67.0%
1 330
33.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 670
67.0%
1 330
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 670
67.0%
1 330
33.0%

feat.g_y
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
671 
1
329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 671
67.1%
1 329
32.9%

Length

2022-11-23T15:35:12.901004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-23T15:35:13.088349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 671
67.1%
1 329
32.9%

Most occurring characters

ValueCountFrequency (%)
0 671
67.1%
1 329
32.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 671
67.1%
1 329
32.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 671
67.1%
1 329
32.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 671
67.1%
1 329
32.9%

feat.g_z
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
659 
1
341 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 659
65.9%
1 341
34.1%

Length

2022-11-23T15:35:13.260698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-23T15:35:13.478392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 659
65.9%
1 341
34.1%

Most occurring characters

ValueCountFrequency (%)
0 659
65.9%
1 341
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 659
65.9%
1 341
34.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 659
65.9%
1 341
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 659
65.9%
1 341
34.1%

Interactions

2022-11-23T15:35:04.516437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:34:57.422608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:34:58.826590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:00.147809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:01.538327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:02.933636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:04.753778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:34:57.676353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:34:59.065194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:00.390000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:01.788349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:03.192089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:04.955272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:34:57.899005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:34:59.262821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:00.601493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:02.009397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:03.416255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:05.187453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:34:58.132840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:34:59.472826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:00.825439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:02.247019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:03.801745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:05.414338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:34:58.347342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:34:59.688962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:01.086666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:02.477038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:04.055816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:05.644755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:34:58.591699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:34:59.927792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:01.321691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:02.722003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T15:35:04.296332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-11-23T15:35:13.648391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-23T15:35:13.978055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-23T15:35:14.384934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-23T15:35:14.730971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-23T15:35:15.048973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-23T15:35:15.342546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-23T15:35:05.986196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-23T15:35:06.477423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

responsefeat.afeat.bfeat.dfeat.efeat.ffeat.hfeat.ifeat.c_afeat.c_bfeat.c_cfeat.c_dfeat.g_xfeat.g_yfeat.g_z
01-0.681427-5.4936980.0-0.800615-4.42760210.254199-0.8280730100001
110.309468-5.5599331.0-1.155514-0.7990949.084749-1.1096980001100
215.676125-4.0269701.0-3.396331-0.6319668.753848-3.4174170100010
311.211525-4.1982631.0-1.894569-16.27326212.191295-1.9048011000010
411.387863-7.8240141.04.696980-22.2088779.6266864.7159030010001
516.145195-2.4391400.0-0.57483011.64260912.362962-0.5214230010010
612.382749-3.6254110.01.326984-4.1488819.2261221.2876181000001
71-2.795184-0.3756891.0-0.869053-2.9948627.973038-0.8393260010100
80-1.060559-2.9722030.00.719649-15.54374812.8931240.7185030100001
91-0.336986-4.6704391.0-0.6054543.0603999.803020-0.5486100100010
responsefeat.afeat.bfeat.dfeat.efeat.ffeat.hfeat.ifeat.c_afeat.c_bfeat.c_cfeat.c_dfeat.g_xfeat.g_yfeat.g_z
99013.027287-5.6457091.0-4.847993-8.07024612.043355-4.8942860100100
9911-2.222620-2.6117330.00.735233-2.87674110.0907260.8165451000100
99202.363733-3.6298010.0-5.109591-7.5781629.301541-5.1199360100010
99300.360079-5.1051570.0-1.393937-21.57559610.537327-1.3978650001010
99401.939686-5.9200130.00.098981-17.42110511.7055790.0848550100001
99500.730074-3.8850350.0-3.356949-12.80334411.204110-3.3966730100001
99614.211548-3.6172530.02.0349956.9957539.2080892.0697521000001
9971-3.053301-3.5838301.01.929012-7.0131057.6378621.8563560010001
9981-0.567850-3.1947161.0-1.8497124.20481611.725868-1.8624660010001
99910.252428-4.6907281.01.742044-4.5640317.9097091.7470370001010